A hyperparameter optimization framework
- python
- machine-learning
- parallel
- distributed
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A hyperparameter optimization framework
Fast and Accurate ML in 3 Lines of Code
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
AutoML library for deep learning
Automated Machine Learning with scikit-learn
Distributed Asynchronous Hyperparameter Optimization in Python
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Lightning ⚡️ fast forecasting with statistical and econometric models.
Scalable and user friendly neural :brain: forecasting algorithms.
AI Infra / AI Orchestration / AI Control Plane
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Automatic architecture search and hyperparameter optimization for PyTorch
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Hyper-parameter optimization for sklearn
Hyperparameter Experiments with TensorFlow and Keras
MLBox is a powerful Automated Machine Learning python library.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.
An autoML framework & toolkit for machine learning on graphs.
Use evolutionary algorithms instead of gridsearch in scikit-learn
Machine Learning toolbox for Humans
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.